1. Field of the Invention
The present invention relates generally to a fingerprint recognition method, and more particularly to a method for extracting the ridge orientations, and core and delta positions of a fingerprint, by using a ridge orientation model with the regional ridge orientation information and entire ridge orientation information of a fingerprint image.
2. Description of the Prior Art
As well known to those skilled in the art, fingerprint recognition is one of security authentication technologies based on biological information. A fingerprint recognition technology is used to recognize a fingerprint having unique features for each person by an image processing method, and determine whether the recognized fingerprint is a registered person's. In the fingerprint recognition technology, the most significant aspect is a process of extracting the minutiae of a fingerprint and generating fingerprint feature data. The process of generating the fingerprint feature data is described in brief as follows. First, a fingerprint acquisition device reads a fingerprint and obtains a fingerprint image. The fingerprint image obtained by the fingerprint acquisition device is divided into a plurality of regions. Thereafter, the orientation values of fingerprint ridges are extracted according to the regions, and then the grayscale values of the ridges are binarized using directional masks. Further, each ridge is thinned into a single line (or skeleton), and minutiae are extracted from thinned ridges. In this case, erroneous minutiae (pseudo minutiae) are removed from the minutiae, and the positions and directions of the correct minutiae are formed into fingerprint feature data.
FIG. 1a is a view showing the bifurcation and ending point of fingerprint minutiae, and FIG. 1b is a fingerprint image view showing the core and delta of fingerprint minutiae.
Referring to FIG. 1a, a bifurcation 21 and an ending point 22 are used as the minutiae of a fingerprint. The bifurcation 21 is a point where the fingerprint ridge is branched, and the ending point 22 is a point where the fingerprint ridge is terminated. Further, a core 23 and a delta 24 shown in FIG. 1b are also used as the minutiae of a fingerprint. The numbers of the cores and the deltas are only zero, one, or two each in any one fingerprint, and there is no other numbers of the cores and the deltas in any one fingerprint. The core 23 and the delta 24 may be recognized with the naked eyes, and have been long used as the references of various fingerprint-classifying methods.
FIG. 2 is a flowchart of a conventional fingerprint minutia extracting method. FIG. 3a is an example view showing eight directional masks, and FIG. 3b is an example view showing the application of directional masks to a fingerprint region.
With reference to FIG. 2, there will be described in detail a conventional fingerprint minutia extracting method, particularly a method for extracting the ending point and bifurcation of fingerprint ridges as minutiae. A fingerprint image is inputted through a fingerprint acquisition device at step 11. At step 12 of extracting and correcting a ridge orientation, the entire fingerprint image is divided into square regions, each with a predetermined size. An orientation having a smallest brightness variation in each region is designated as a ridge orientation in a corresponding region. In order to correct the ridge orientation, the orientation value of a corresponding region is determined by averaging the orientation of the corresponding region and the orientations of its surrounding regions. At step 13, a binarization process is performed using orientation information as follows. For each pixel P, each grayscale, value in a region, of which the center is the pixel P and the size is the same size as a directional mask, is multiplied by a correspondent coefficient of directional mask corresponding to the direction at the center point of the region among masks 25a to 25h (a mask 25h is used in FIG. 3b). At this time, if the summation of the multiplied result is a positive value, the pixel P is in a ridge of the fingerprint and the pixel P is converted to “1”. If it is a negative value, the pixel P is in a valley of the fingerprint, and the pixel P is converted to “0”. FIG. 4 is a view showing a thinning process, and FIGS. 5a and 5b are views showing the principle of finding minutiae in a thinned image. At thinning step 14, in order to determine a skeleton of the ridge in the binary image having a constant ridge width as shown in FIG. 4, an outline of the ridge is converted into a valley until the width of the ridge becomes to “1” (in other words, when a skeleton of the ridge remains only). Finally, at the step 15 of extracting minutia positions and its directions, with respect to a point at which a value is “1” in the thinned image, the number of regions where a transition between “1” and “0” in adjacent arbitrary two points among the neighboring eight points appears is counted (a boundary of regions represented with dotted lines in FIGS. 5a and 5b). When the counted numbers are 2, 4, 6, and 8, respectively, the center points corresponding to the counted numbers are respectively classified into an ending point, a ridge, a bifurcation, and a cross point. FIG. 5a shows an ending point having a ridge direction from the left to the right, and FIG. 5b shows a bifurcation having a ridge direction from the left to the right. Finally, the ending and the bifurcation are used as the most important feature for distinguishing the fingerprints from one other.
The accuracy of the conventional method is relatively high. However, the conventional method for extracting the ridge orientation is disadvantageous in that it extracts the ridge orientation according to regions divided in the fingerprint image and it determines representative orientations according to regions, such that a fine variation of a orientation in a region cannot be exactly represented. Further, under the provision that the ridge orientation is not rapidly changed, the conventional method evaluates the average of ridge orientations of neighboring regions around each region, and corrects a orientation value using the average. However, such a provision cannot be adapted to regions near a core or a delta where the ridge is rapidly changed in orientation. Moreover, the orientation of a region with a tiny wound or a wrinkle on a fingerprint can be corrected by predicting a ridge orientation from the ridge orientations of neighboring regions. However, if the wounded part is large, it is impossible to find an exact ridge orientation. Such problems are due to a fact that the extraction and correction of the ridge orientation are only based on local image information.
Further, a conventional core and delta extracting method calculates positions of the core and the delta by detecting a directional variation in local regions. Therefore, such a method is disadvantageous in that it cannot extract the exact positions of the core or the delta, or cannot find the exact position at all, in a fingerprint image with a wound near the core or the delta. In addition, the conventional method is also disadvantageous in that it calculates the positions of the core and the delta using the orientation values mainly calculated according to regions, such that accuracy of the positions depends on a size of a region for calculating the orientations.
As described above, the conventional computer-aided method for extracting the ridge orientation and the core and delta positions has the basic limitation for lack of the information about entire ridge flow. As an example, a fingerprint expert knows a variety of types of fingerprints. Even if some regions of the fingerprint are damaged, the fingerprint expert can recognize an entire ridge flow from ridge orientations in undamaged regions of the fingerprint. Thereby, the expert can find a precise ridge orientation of the damaged region, and extract the precise positions of the core and delta, even if the regions near the core and the delta are damaged.
Therefore, in order to solve the above problems in the conventional computer-aided algorithm, there is required a method for extracting minutiae in consideration of the entire shape (flow) of the fingerprint as well as its local region.